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Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test

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Abstract

The performance of all four GOLD scoring functions has been evaluated for pose prediction and virtual screening under the standardized conditions of the comparative docking and scoring experiment reported in this Edition. Excellent pose prediction and good virtual screening performance was demonstrated using unmodified protein models and default parameter settings. The best performing scoring function for both pose prediction and virtual screening was demonstrated to be the recently introduced scoring function ChemPLP. We conclude that existing docking programs already perform close to optimally in the cognate pose prediction experiments currently carried out and that more stringent pose prediction tests should be used in the future. These should employ cross-docking sets. Evaluation of virtual screening performance remains problematic and much remains to be done to improve the usefulness of publically available active and decoy sets for virtual screening. Finally we suggest that, for certain target/scoring function combinations, good enrichment may sometimes be a consequence of 2D property recognition rather than a modelling of the correct 3D interactions.

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Abbreviations

CCDC:

Cambridge Crystallographic Data Centre

PDB:

Protein Data Bank

RMSD:

Root mean square deviation

VS:

Virtual screening

ROC:

Receiver operating characteristic

AUC:

Area under curve

DUD:

Directory of useful decoys

References

  1. Jones G, Willett GP, Glen RC (1995) J Mol Biol 245:43–53

    Article  CAS  Google Scholar 

  2. Jones G, Willett P, Glen RC, Leach AR, Taylor RJ (1997) Mol Biol 267:727–748

    Article  CAS  Google Scholar 

  3. Sousa SJ, Alexandrino PS, Ramos MJ (2006) PROTEINS 65:15–26

    Article  CAS  Google Scholar 

  4. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) J Comput Aided Mol Des 11:425–445

    Article  CAS  Google Scholar 

  5. Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) PROTEINS 52:609–623

    Article  CAS  Google Scholar 

  6. Mooij WTM, Verdonk M (2005) PROTEINS 61:272–287

    Article  CAS  Google Scholar 

  7. Korb O, Stützle T, Exner TE (2009) J Chem Inf Model 49(1):84–96

    Article  CAS  Google Scholar 

  8. Hartshorn MJ, Verdonk ML, Chessari G, Brewerton SC, Mooij WTM, Mortensen PN, Murray CW (2007) J Med Chem 50:726–741

    Article  CAS  Google Scholar 

  9. http://www.ccdc.cam.ac.uk/products/life_sciences/gold/

  10. Hendlich M, Bergner A, Günther J, Klebe G (2003) J Mol Biol 326:607–620

    Article  CAS  Google Scholar 

  11. Verdonk ML, Chessari G, Cole JC, Hartshorn MJ, Murray CW, Nissink JWM, Taylor RD, Taylor R (2005) J Med Chem 48:6504–6515

    Article  CAS  Google Scholar 

  12. Huang N, Shoichet BK, Irwin JJ (2006) J Med Chem 49:6789–6801

    Article  CAS  Google Scholar 

  13. Good AC, Oprea TI (2008) J Comput Aided Mol Des 22:169–178

    Article  CAS  Google Scholar 

  14. Dönneke D, Schweintz A, Stürzebecher A, Steinmetzer P, Schuster M, Stürzebecher U, Nicklisch S, Stürzebecher J, Steinmetzer T (2007) Bioorg & Med Chem Lett 17:3322–3329

    Google Scholar 

  15. Bender A, Glen RC (2005) J Chem Inf Model 45(5):1369–1375

    Google Scholar 

  16. Verdonk M, Giangreco I, Hall R, Korb O, Mortensen P, Murray CW (2011) J Med Chem 54:5422–5431

    Article  CAS  Google Scholar 

  17. Sutherland SJ, Nandigam RK, Erikson JA, Vieth M (2007) J Chem Inf Model 47:2293–2302

    Article  CAS  Google Scholar 

  18. Verdonk ML, Mortenson PN, Hall RJ, Hartshorn MJ, Murray CW (2008) J Chem Inf Model 48:2214–2225

    Article  CAS  Google Scholar 

  19. Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) J Med Chem 49(20):534–553

    Article  CAS  Google Scholar 

  20. Jain AN (2009) J Comput Aided Mol Des 23:355–374

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England. We also thank Dr Colin Groom for valuable comments regarding the manuscript.

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Correspondence to John W. Liebeschuetz.

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Liebeschuetz, J.W., Cole, J.C. & Korb, O. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. J Comput Aided Mol Des 26, 737–748 (2012). https://doi.org/10.1007/s10822-012-9551-4

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  • DOI: https://doi.org/10.1007/s10822-012-9551-4

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